Advancing eLearning with Data

Abstract: Electronic learning (eLearning) has witnessed explosive growth over the past decade, with learning scenarios from corporate training to higher education now supporting full course delivery online. Despite the conveniences offered to both learners and instructors, eLearning has been widely criticized for poorer learning outcomes compared with those obtained by traditional classrooms, in terms of metrics such as engagement and knowledge transfer. Today’s learning technology systems, however, can be equipped with infrastructure to capture a plethora of data about learners as they take courses online, such as their multimedia clickstream measurements, assessment responses, and social interactions with one another. The availability of this data presents the opportunity to develop fine-granular models of learners, which can in turn drive methodologies at the intersection of machine learning, data science, and the science of learning aimed at improving learning outcomes.

In this talk, I will describe the research I have undertaken to improve the quality of eLearning, while at my company Zoomi Inc., during my graduate studies, and as an instructor. Broadly speaking, my team’s investigations have followed three, complementary thrusts: Predictive Learning Analytics (PLA), Social Learning Networks (SLN), and Deep Learning Personalization (DLP). PLA involves developing algorithms for early detection of learning outcomes that model from behavioral data and can provide actionable analytics to instructors. SLN involves quantifying how learners seek and disseminate knowledge socially, and designing algorithms to predict and optimize these processes. DLP involves systematically dividing course content into micro-segments, and combining topic, learner, and reinforcement learning models to adapt the material presented to individual learners in real time. In describing these thrusts, I will point out results Zoomi has witnessed in deployments to different learning scenarios, and will also discuss open research questions.

Bio: Dr. Christopher G. Brinton is the Head of Advanced Research at Zoomi Inc, an AI-driven learning technology company he co-founded in 2013, and a Lecturer in Electrical Engineering at Princeton University. His research focus is developing systems and methods to improve the quality of student learning, through predictive learning analytics, social learning networks, and individualization. Chris co-authored the book The Power of Networks: Six Principles that Connect our Lives, and has reached over 250,000 students through MOOCs based on his book. A recipient of the 2016 Bede Liu Best Dissertation Award in Electrical Engineering, Chris received his PhD from Princeton in 2016, his Master’s from Princeton in 2013, and his BSEE from The College of New Jersey (valedictorian and summa cum laude) in 2011, all in Electrical Engineering.